A low-carbon and economical dispatch method and device for a hydrogen-electricity combined system

Abstract

This invention discloses a low-carbon economic dispatch method and apparatus for a hydrogen-electricity combined system, belonging to the field of electrical control engineering technology. The method aims to minimize system operating costs under carbon tax considerations, using discretized linear dynamic energy flow equations as constraints for the operation of the hydrogen-electricity network. It constructs a low-carbon economic dispatch model for the hydrogen-electricity combined system, which can both characterize the dynamic characteristics of the hydrogen-natural gas transportation process and meet the requirements of optimization problem solving. It can accurately reflect the operating status of the hydrogen-natural gas system by tracking changes in gas composition during transportation. While preserving the model's characteristics, it reduces the computational cost of real-time solving of the nonlinear dynamic energy flow model. The method uses the low-carbon economic dispatch model of the hydrogen-electricity combined system for dispatching, ensuring that the combined system always operates within safe boundaries, improving the system's renewable energy absorption capacity, and reducing system operating costs and carbon emissions.

Description

Technical Field

[0001] This invention belongs to the field of electrical control engineering technology, and more specifically, relates to a low-carbon economic dispatching method and device for a hydrogen-electricity combined system. Background Technology

[0002] With the maturation of hydrogen production technology and the development of comprehensive hydrogen energy utilization, hydrogen energy, as a clean energy source, is playing an increasingly important role in new power systems. By integrating heterogeneous energy sources such as electricity, hydrogen, and gas through energy conversion hubs like electric hydrogen production and hydrogen-blended gas turbines, a mixed hydrogen-electricity system can be constructed. This system can leverage the differences in characteristics between different energy sources, improve the utilization rate of energy infrastructure, enhance the overall self-healing capability of the energy supply system, and ensure the reliability of energy supply. Simultaneously, it provides a valuable opportunity for the traditional oil and gas industry to participate in integrated energy industries, achieve profit growth, and effectively promote the low-carbon transformation of the energy system. Research shows that due to the differences in the physicochemical properties of hydrogen and natural gas, the pressure, flow rate, and other operating conditions change significantly when using existing infrastructure to blend natural gas and hydrogen, significantly impacting gas quality and transmission safety, and also introducing challenges to the operation of the combined system. Therefore, a low-carbon economic dispatch method for mixed hydrogen-electricity systems that considers gas composition tracking is urgently needed to provide a theoretical basis for guiding the optimized operation, planning and design, and market mechanisms of the combined system.

[0003] Current research on the optimal scheduling of hydrogen-electric combined systems primarily employs steady-state energy flow models. These models neglect the transition time of the hydrogen-blended network, which can last for tens of hours, and lack quantitative characterization of gas composition differences across time and space. This leads to significant discrepancies between the calculated operating state and the actual state, resulting in scheduling solutions that are not truly optimal or even infeasible. Furthermore, steady-state models fail to account for pipeline storage capacity, thus failing to fully leverage pipeline flexibility to improve system reliability. Dedicated gas pipeline simulation software employs numerical simulations, which contain numerous highly nonlinear partial differential equations, making it difficult to coordinate with other forms of energy flow optimization within the integrated energy system. For these reasons, scheduling methods for hydrogen-electric combined systems often suffer from low efficiency. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a low-carbon and economical scheduling method and apparatus for a mixed hydrogen-electricity system. The purpose is to solve the technical problem that existing scheduling methods for mixed hydrogen-electricity systems suffer from reduced scheduling efficiency because the scheduling model cannot characterize the dynamic characteristics of the mixed hydrogen-natural gas transportation process.

[0005] To achieve the above objectives, according to one aspect of the present invention, a low-carbon economic dispatch method for a hydrogen-electricity combined system is provided, comprising:

[0006] S1: Taking into account the dynamic transport process of mixed gas and the effect of gas parameter distribution, construct a set of partial differential equations corresponding to the dynamic energy flow of the pipeline network;

[0007] S2: Replace the corresponding nonlinear terms in the partial differential equations corresponding to the dynamic energy flow of the pipeline network with the velocity reference value, the flow rate reference value, and the hydrogen mass flow rate, so as to reconstruct the partial differential equations into a discretized linear dynamic energy flow equation suitable for solving the optimal scheduling problem.

[0008] S3: Taking the minimum system operating cost under the consideration of carbon tax as the optimization objective, and using the discretized linear dynamic energy flow equation as the operating constraint of the hydrogen-electricity combined system, a low-carbon economic dispatch model is constructed. Y represents the total operating cost, including the operating expenses of coal-fired power units (C). CFU Renewable energy cost reduction C RCUR Electricity load reduction cost C LCUR Gas purchase cost Carbon emission costs

[0009] S4: Solve the low-carbon economic dispatch model of the hydrogen-electric combined system to obtain an executable joint system dispatch scheme;

[0010] S5: Execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity joint system.

[0011] In one embodiment, in S1: the partial differential equations corresponding to the constructed single-pipe dynamic energy flow include: the momentum conservation equation of the mixed gas, the continuity equation, and the hydrogen component transport equation, as follows:

[0012]

[0013] Where t and x represent the axial distance and time along the pipe, respectively; ρ is the density of the mixed gas, in kg / m³. 3 u is the velocity of the mixed gas, in m / s; p is the pressure, in Pa; λ is the hydraulic friction coefficient, dimensionless; d is the pipe diameter, in m; τ k Let be the mass fraction of the k-th component in the gas mixture, i.e., the mass fraction of hydrogen.

[0014] In one embodiment, the constraints on the gas density, pressure, and composition of the pipeline source node are as follows:

[0015]

[0016] Where, ρ i,t ρ represents the gas density at node i at time t. src,0 p represents the gas density of the gas source. i,tp represents the air pressure at node i at time t. src,0 τ represents the air pressure of the gas source. k i,t τ represents the hydrogen mass fraction at node i at time t. k src,0 T represents the mass fraction of hydrogen in the gas source. N Indicates the total number of scheduling periods;

[0017] Flow constraints at load nodes in the last segment of the pipeline network:

[0018]

[0019] Among them, M i,t Let be the mass flow rate at node i at time t. Let be the mass flow rate demand of hydrogen-mixed natural gas at load node i at time t. Let τ be the natural gas mass flow rate requirement at node i at time t, without considering hydrogen blending. k i,t Let q be the mass fraction of hydrogen in the hydrogen-mixed natural gas at node i at time t. g q h These represent the calorific value of natural gas and hydrogen, respectively.

[0020] Gas flow balance constraints:

[0021]

[0022] Uniform mixing constraint: Where, N pipe Represents the number of pipes connected to node i; Let be the mass flow rate into / out of node i via the j-th pipe connected to node i at time t; Let be the mass flow rate of the mixed hydrogen natural gas injected at node i at time t; Let be the mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i; Let be the mass fraction of hydrogen in the gas flowing into / out of node i via the j-th pipe connected to node i at time t. The hydrogen mass fraction at the load node i; The required mass flow rate of hydrogen-mixed natural gas for the load at node i; The mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i.

[0023] In one embodiment, the linearized and reconstructed partial differential equation for single-pipe flow is as follows:

[0024]

[0025] Where M is the mass flow rate of the mixed gas, in kg / s; A is the cross-sectional area of ​​the pipe, in m². 2 Z is the compressibility factor; R is the gas constant of the gas mixture, in J / (kg·K); T is the thermodynamic temperature, in K; u b This is a reference value for gas flow rate, in m / s; m k is the hydrogen mass flow rate, in kg / s; 'a' is a coefficient related to the mass flow rate of the mixed gas.

[0026] The reconstructed gas flow balance constraints and uniform mixing constraints are as follows:

[0027]

[0028] in, Let t be the mass flow rate of hydrogen gas flowing into / out of node i via the j-th pipe connected to node i at time t. Let be the hydrogen mass flow rate at the load node i; Let t be the baseline value of the gas flow velocity exiting node i through the j-th pipe connected to node i at time t.

[0029] In one embodiment, before S2, the method further includes: based on the stability conditions in numerical analysis, reasonably dividing the numerical calculation differential grid, and then using a sequential algorithm to pre-solve the dynamic energy flow of the nonlinear pipeline network to obtain the velocity reference value and the flow reference value; the step size of the differential grid of the energy flow equation satisfies the following conditions:

[0030]

[0031] Among them, u i,t Let u be the gas velocity at node i at time t; i-1,t Let Δt be the gas velocity at node i-1 at time t, where Δt is the time step and Δx is the spatial step.

[0032] In one embodiment, a sequential algorithm is used to pre-solve the dynamic energy flow of the nonlinear pipeline network to obtain velocity and flow rate reference values, including:

[0033] 1) Solve the steady-state energy flow equation using Newton's iterative method, and use it as the initial condition for dynamic energy flow calculation;

[0034] 2) Solve the pressure-flow equation in the nonlinear pipeline dynamic energy flow using the Newton-Raphson iteration method to obtain the pressure p. t and mass flow rate M t Then, the flow rate, gas parameters, and load flow requirements are solved.

[0035] 3) Use the flow velocity u obtained in step 2) tSolve for the component distribution τ of the gas mixture at time t. k,t ;

[0036] 4) Using the state variable p at time t t M t Update the gas parameters and proceed to step 3) to perform energy flow calculation at time t+1, finally obtaining the velocity reference value and flow reference value in the linearized process.

[0037] In one embodiment, S4 includes:

[0038] S41: Let the iteration number r = 1;

[0039] S42: Relaxing gas network operation constraints, solving for the optimal energy flow of the combined system to obtain output from hydrogen production via electricity and hydrogen-blended gas turbines.

[0040] S43: Will Converted to nodal hydrogen injection volume Gas demand at load nodes As boundary conditions for numerical simulation of mixed hydrogen natural gas networks;

[0041] S44: Obtaining the baseline flow velocity u through numerical simulation of a hydrogen-mixed natural gas network b,r Parameter a, gas parameters Z and Rλ are used to construct a linear dynamic energy flow model for a mixed hydrogen natural gas network;

[0042] S45: Call the solver to solve the low-carbon economic dispatch problem of the hydrogen-electric combined system and output the calculated hydrogen production and hydrogen-blended gas turbine power. Convert to boundary conditions Another numerical simulation of the hydrogen-to-natural gas network was performed to solve for the velocity distribution u. r ;

[0043] S46: Utilizing velocity distribution u r Calculate the error ε between the actual flow velocity and the reference flow velocity ε=||u r -u b,r ||;

[0044] S47: Determine if the convergence condition ε≤ε0 is met: If it is met, terminate the calculation; if not, update the boundary conditions. r = r + 1, return to S43 to enter the next iteration.

[0045] According to another aspect of the present invention, a low-carbon economic dispatch method for a hydrogen-electricity combined system is provided, comprising:

[0046] The first construction module is used to take into account the dynamic transport process of mixed gas and the effect of gas parameter distribution, and to construct the partial differential equation system corresponding to the dynamic energy flow of the pipeline network.

[0047] The reconstruction module is used to replace the corresponding nonlinear terms in the partial differential equations corresponding to the dynamic energy flow of the pipeline network with the velocity reference value, the flow reference value, and the hydrogen mass flow rate, so as to reconstruct the partial differential equations into a discretized linear dynamic energy flow equation suitable for solving the optimal scheduling problem.

[0048] The second module is used to construct a low-carbon economic dispatch model for the hydrogen-electric combined system, with the goal of minimizing system operating costs under carbon tax considerations and using discretized linear dynamic energy flow equations as constraints for the operation of the hydrogen-electric combined system. Y represents the total operating cost, including the operating expenses of coal-fired power units (C). CFU Renewable energy cost reduction C RCUR Electricity load reduction cost C LCUR Gas purchase cost Carbon emission costs

[0049] The solution module is used to solve the low-carbon economic scheduling model of the hydrogen-electric combined system and obtain an executable scheduling scheme for the combined system.

[0050] The scheduling module is used to execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity joint system.

[0051] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0052] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0053] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0054] (1) This invention provides a low-carbon economic dispatch method for a mixed hydrogen-electricity system. The optimization objective is to minimize the system operating cost under the consideration of carbon tax. The discretized linear dynamic energy flow equation is used as the operating constraint of the mixed hydrogen network. A low-carbon economic dispatch model for the mixed hydrogen-electricity system is constructed. This model can not only characterize the dynamic characteristics of the mixed hydrogen natural gas transportation process, but also meet the requirements of solving optimization problems. It can track the changes in gas composition during transportation and more accurately reflect the operating status of the mixed hydrogen natural gas system. While preserving the model characteristics to the maximum extent, the computational cost of solving the nonlinear dynamic energy flow model in real time is reduced, ensuring the efficiency and economy of the joint system dispatch. The low-carbon economic dispatch model of the mixed hydrogen-electricity system is solved for dispatching, ensuring that the joint system always operates within the safety boundary, improving the renewable energy absorption capacity of the system, and reducing the operating cost and carbon emissions of the system.

[0055] (2) This scheme introduces a hydrogen component transport equation in the dynamic energy flow of a single pipeline for gas component tracking, which is beneficial to reflecting the dynamic spatiotemporal distribution characteristics of heterogeneous gas components, and thus constructs the operating constraints of the mixed hydrogen network.

[0056] (3) This scheme takes into account the gas quality inhomogeneity and calorific value change caused by hydrogen doping. It designs uniform mixing constraints, gas component conservation constraints and load node boundary conditions that take into account calorific value changes. The advantage is that it can reflect the changes in the gas network operation state caused by the heterogeneous gas mixed with hydrogen.

[0057] (4) This scheme introduces a reference flow rate and hydrogen mass flow rate to handle the bilinear terms in the constraints, reconstructs the linearized pipeline dynamic energy flow, significantly improves the mathematical properties of the pipeline energy flow model, and makes the model applicable to large-scale optimization problems.

[0058] (5) This scheme gives the stability condition that the spatiotemporal difference grid step size division should satisfy. Based on this condition, the partial differential equation is discretized, which can avoid numerical oscillation in the gas network simulation of the scheduling model, or even the situation where there is no feasible solution.

[0059] (6) The sequential algorithm in this scheme solves the pressure-flow rate and mixed gas component distribution parts of the unlinearized pipeline energy flow equation in sequence, thereby obtaining the velocity and flow rate reference values ​​for linearization. The advantage is that the accuracy of the linear approximation of the gas network is improved by iterative solution.

[0060] (7) The scheduling solution algorithm proposed in this scheme improves the solution accuracy of the mixed hydrogen network through iteration, and also makes the joint scheduling optimization result closer to the actual optimal value. Attached Figure Description

[0061] Figure 1 A flowchart of a low-carbon economic dispatch method for a hydrogen-gas-electric combined system provided in Embodiment 1 of the present invention;

[0062] Figure 2 The flowchart of the sequential algorithm for solving the linearized benchmark value provided in Embodiment 1 of the present invention;

[0063] Figure 3 This is the iterative process for solving the optimization scheduling model provided in Embodiment 1 of the present invention;

[0064] Figure 4 This is a schematic diagram of a branchless gas network for a single electric hydrogen production unit in Embodiment 1 of the present invention;

[0065] Figure 5 This is a schematic diagram of a hydrogen-electricity combined system consisting of a 6-node power system and a 12-node natural gas system in Embodiment 1 of the present invention;

[0066] Figure 6 and Figure 7 The results are calculated for hydrogen mass fraction and source node mass flow rate using steady-state energy flow and the proposed linear dynamic energy flow model in Example 1 of this invention, respectively.

[0067] Figure 8 This is a schematic diagram of a low-carbon economic dispatching device for a mixed hydrogen-electricity system provided in Embodiment 2 of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0069] Example 1

[0070] Figure 1 A flowchart of a low-carbon economic dispatch method for a hydrogen-electric hybrid system provided in this embodiment; see also Figure 1 This embodiment provides a detailed description of a low-carbon economic dispatch method for a hydrogen-electricity combined system, which includes the following steps.

[0071] S1. Obtain conventional technical parameters of the natural gas pipeline network, including network topology, operating pressure level, temperature, pipeline inner diameter, absolute roughness of the pipeline inner wall, and data sets of gas well pressure and gas load. Obtain power system parameters, including grid topology, unit parameters, renewable energy predicted output data sets, and power load data sets. Obtain parameters of the gas-electric coupling link, including parameters of the electro-hydrogen production unit and hydrogen-blended gas turbine parameters. Considering the dynamic transmission process of the mixed gas and the effect of gas parameter distribution, establish a partial differential equation for the flow of mixed hydrogen and natural gas in a single pipeline that reflects the gas transmission characteristics in a single pipeline. Also, establish dynamic energy flow equations for the pipeline network based on the network topology and node types.

[0072] S2 reconstructs the nonlinear partial differential dynamic energy flow equations into discretized linear dynamic energy flow equations suitable for solving the optimal scheduling problem. Specifically, the velocity and flow rate reference values ​​are first determined, and then the corresponding nonlinear terms in the partial differential equations corresponding to the pipeline network's dynamic energy flow are replaced using these reference values ​​and the hydrogen mass flow rate. The process of determining the velocity and flow rate reference values ​​is as follows: Based on the stability conditions in numerical analysis, the numerical computation grid is reasonably divided to save computational resources, improve computational efficiency, and suppress numerical oscillations; a sequential algorithm is used for pre-solution to obtain the velocity and flow rate reference values ​​in the linearization process.

[0073] S3. Establish a low-carbon economic dispatch model for a hydrogen-electric hybrid system. Its optimization objective is to minimize the system operating cost under the consideration of carbon tax. The constraints include equipment operation constraints, power grid operation constraints, hydrogen-electric hybrid network operation constraints, electricity-gas energy conversion constraints, and carbon quota constraints.

[0074] S4, under the scenario of fluctuations in renewable energy output and changes in the hydrogen doping ratio at hydrogen injection points, obtains an executable joint system scheduling scheme by solving the optimization scheduling model through online pre-decision.

[0075] S5: Execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity combined system and make full use of the system's flexibility resources.

[0076] Furthermore, the pipe flow equation reflecting the transmission characteristics of mixed hydrogen and natural gas in a single pipeline and the pipeline network dynamic energy flow equation considering gas composition tracking are constructed as follows:

[0077] The pipe flow equations reflecting the transport characteristics of hydrogen-mixed natural gas in a single pipeline include the momentum conservation and continuity equations of the mixed gas, as well as the hydrogen component transport equations reflecting the hydrogen diffusion process:

[0078]

[0079] Where t and x represent the axial distance and time along the pipe, respectively; ρ is the density of the mixed gas, in kg / m³. 3 u is the velocity of the mixed gas, in m / s; p is the pressure, in Pa; λ is the hydraulic friction coefficient, dimensionless; d is the pipe diameter, in m; τ k The mass fraction of hydrogen in the gas mixture; m k is the mass flow rate of hydrogen in the gas mixture, in kg / s; M is the mass flow rate of the gas mixture, in kg / s.

[0080] The dynamic energy flow equations for pipeline networks considering gas composition tracking include, in addition to the single-pipe flow equations, given operating conditions, pipeline topology, and component parameters:

[0081] The originating station is responsible for receiving purified natural gas from mine purification plants or other gas sources, serving as the source node of the pipeline network. In this stage, the gas pressure, density, and composition remain essentially constant.

[0082]

[0083] Where, ρ i,t ρ represents the gas density at node i at time t. src,0 p represents the gas density of the gas source. i,t p represents the air pressure at node i at time t. src,0 τ represents the air pressure of the gas source. ki,t τ represents the hydrogen mass fraction at node i at time t. k src,0 T represents the mass fraction of hydrogen in the gas source. N This indicates the total number of scheduling periods.

[0084] The last section of the pipeline network is considered a load node, and its pressure is mainly affected by the flow rate required by the gas distribution station.

[0085]

[0086] Among them, M i,t Let be the mass flow rate at node i at time t; Let be the mass flow rate demand of hydrogen-mixed natural gas at load node i at time t; Let τ be the natural gas mass flow rate requirement at node i at time t, without considering hydrogen blending; k i,t Let q be the mass fraction of hydrogen in the hydrogen-mixed natural gas at node i at time t; g q h These represent the calorific value of natural gas and hydrogen, respectively.

[0087] At the pipeline junction, the mass of each gas component is conserved, and a gas flow balance constraint is constructed as shown in equations (7)-(8). Simultaneously, the gas with different hydrogen contents at the junction is fully mixed, constructing a uniform mixing constraint (9).

[0088]

[0089]

[0090] Where, N pipe Represents the number of pipes connected to node i; Let be the mass flow rate into / out of node i via the j-th pipe connected to node i at time t; Let be the mass flow rate of the mixed hydrogen natural gas injected at node i at time t; Let be the mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i; Let be the mass fraction of hydrogen in the gas flowing into / out of node i via the j-th pipe connected to node i at time t. The hydrogen mass fraction at the load node i; The required mass flow rate of hydrogen-mixed natural gas for the load at node i; The mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i.

[0091] In the dynamic energy flow equations of the mixed hydrogen natural gas network, the parameter distribution effects caused by different gas compositions are as follows:

[0092] The hydrogen blending ratio affects the equation of state, gas constant, compressibility factor, and specific heat at isobaric pressure.

[0093] R = τ k R h +(1-τ k )R g (10)

[0094]

[0095] T R,subs =T subs / T subs,cr subs=NG,H2 (15)

[0096]

[0097] k subs =0.480+1.574ω subs -0.176ω subs 2 (17)

[0098] Among them, R,R g ,R h Let be the gas constant of the mixture / natural gas / hydrogen, in J / (kg·K); ρ NG The density of natural gas / hydrogen; c v This is the integral of hydrogen gas, superscript NG. y This represents the y-th component in natural gas; Let ω be the critical temperature and critical pressure of hydrogen. subs As the eccentricity factor, it reflects molecular polarity; A Z B Z ,T R,subs ,k subs All of them are dimensionless constants.

[0099] In the energy flow calculation of the mixed hydrogen-natural gas network, the gas parameters need to be updated synchronously, that is, the parametric equations and the dynamic energy flow equations should be solved simultaneously. The relationship between the pressure and density of the mixed gas is expressed by the state equation (18):

[0100] p=ρZ(τ,p,T)R(τ)T (18)

[0101] The hydrogen doping ratio affects the gas viscosity, which in turn affects the hydraulic friction coefficient in the pipe flow equation:

[0102]

[0103] Where λ is the hydraulic friction coefficient; ε and D are the pipe roughness and diameter, respectively; Re is the Reynolds number; μ is the dynamic viscosity coefficient, Pa·s; and M is the gas molar mass, g / mol.

[0104] The above parametric equations, which consider gas composition tracking, and the pipeline dynamic energy flow equations together constitute a low-carbon economic dispatch model for the hydrogen-electricity combined system. This model can accurately reflect the dynamic process of the system after hydrogen blending, including changes in state variables and gas parameters such as gas pressure, flow rate, and gas composition.

[0105] Furthermore, the low-carbon economic dispatch model for the hydrogen-electric combined system applies the following linearization method:

[0106] At low hydrogen doping ratios, the hydraulic conditions for natural gas pipeline transportation do not change significantly; therefore, a baseline flow velocity is used to approximate the quadratic term.

[0107]

[0108] When using mass fraction to describe the variation of gas composition with time and space, the hydrogen composition conservation equation (8) contains a large number of bilinear terms τ. k Since the constraint M is difficult to solve directly in the optimization problem, the hydrogen mass flow rate m is used. k Equivalent substitution τ k M, and replace the one containing τ k Constraints. The partial differential equations for single-pipe flow include the pressure-flow equations (22)-(23), and the linearized reconstruction of the hydrogen component transport equation (24) is as follows:

[0109]

[0110] Hydrogen mass fraction τ k The constraints (8)-(9) are reconstructed as follows:

[0111]

[0112] Wherein, the reference flow velocity u b The gas parameters Z, Rλ, and coefficient a are taken as reference steady-state values, or the baseline correction iteration method for dynamic energy flow calculation is used, with the latter generally having better calculation accuracy than the former. In this invention, a numerical simulation method is used for baseline correction iteration. A finite number of iterations are performed during the optimization problem solution process, and the obtained baseline flow velocity value can well meet the calculation accuracy requirements.

[0113] In the low-carbon economic dispatch model of the hydrogen-electric combined system, the differential form is constructed as follows:

[0114]

[0115] In the formula, Δt and Δx represent the time and spatial difference step sizes, respectively. After differential processing, in a hydrogen-to-natural gas network containing M pipeline segments and N nodes (including p source nodes and q load nodes), T N There are (N+4M)×T time periods. N There are N×T state variables, including N×T N Node air pressure, 2M×T N Mass flow rate, 2M×T N The mass flow rate of hydrogen gas. Meanwhile, the governing equations and boundary conditions total (N+4M)×T. N One, including M×T N A continuity equation, M×T N A momentum equation, (Npq)×T N A mass conservation equation, M×T N The transport equations for each component are given by (Npq)×T. N The conservation equation for hydrogen components is (M-N+q)×T N Equation for uniform mixing at intersection points, p×T N Pressure conditions at each source node, q×T N Given a load mass flow rate condition, p×T N The hydrogen doping ratio condition for each source node is such that the number of equations equals the number of state variables, and can be solved.

[0116] Differential mesh generation must satisfy the stability conditions of numerical analysis. The CFL condition is used to determine whether the finite element calculation results converge. The Coulomb number calculation method for the spatiotemporal mesh in a one-dimensional pipeline is as follows:

[0117]

[0118] Among them, the Courant number γ cfl This represents the number of grid cells traversed by the fluid particles within the time step. When the solution speed exceeds the propagation speed of physical disturbances, it ensures that all disturbances are captured. A larger Courant number (i.e., a larger time step and a smaller spatial step) results in faster convergence but lower stability, and oscillations may occur in the steady-state phase. Conversely, a smaller Courant number (i.e., a smaller time step and a larger spatial step) results in slower convergence but higher stability, and oscillations may occur in the unsteady-state phase. For the explicit upwind difference method of one-dimensional linear convection equations, C is typically taken as 1.

[0119] Furthermore, the condition for the hydrogen component transport equation to not exhibit numerical oscillations is constructed as follows:

[0120]

[0121] (K+1)τ k i,t +(1-K)τ k i-1,t -τ ki,t-1 -τ k i-1,t-1 =0 (30)

[0122] (K+1)(τ k i,t -τ k i,t-1 )+(1-K)(τ k i-1,t -τ k i-1,t-1 )+K(τ k i,t-1 -τ k i-1,t-1 )=0(31)

[0123]

[0124] Specifically, satisfying K⁻¹ > 0 avoids numerical oscillations. More specifically, when the hydrogen doping ratio is disturbed at time t, the system is in a steady state before the disturbance, τ k i-1,t-1 =τ k i,t-1 This means that the gas composition is uniform at both ends of the pipeline. When K > 1, τ k i,t -τ k i,t-1 With τ k i-1,t -τ k i-1,t-1 If the gas composition changes at the end of the pipeline with the same sign, the trend is consistent with that at the beginning, which is in accordance with physical laws. Otherwise, numerical oscillations will occur.

[0125] According to the above criteria, the difference step size of the dynamic energy flow equation should satisfy the following condition:

[0126]

[0127] Furthermore, Figure 2 The flowchart of the sequential algorithm for solving the linearized benchmark value provided in Embodiment 1 of the present invention is as follows: In the numerical simulation for solving the linearized benchmark value, the sequential algorithm is constructed as follows:

[0128] In the dynamic model of the pipeline network, the mass flow rate demand of the load nodes With hydrogen mass fraction τ k Related, reference flow rate u b The state variables, such as pressure p and mass flow rate M, are related to gas parameters Z, R, λ, and the hydrogen mass fraction τ. kThis involves high-order equations related to multiple state variables. The linearized equations still contain the aforementioned parameters, leading to coupling between constraints and making the optimization problem difficult to solve. This invention proposes a sequential algorithm to solve the original nonlinear energy flow equations in a certain order, updating the parameters in the linear pipeline network dynamic model. More specifically:

[0129] 1) Solve the steady-state energy flow equation using the Newton-Raphson iteration method as the initial condition for dynamic energy flow calculation.

[0130] 2) Solve the pressure-flow equation in the nonlinear pipeline dynamic energy flow using Newton's iteration method; obtain the pressure p. t and mass flow rate M t Then, the flow velocity u is solved. t Gas parameters such as Z, R, and λ, and load flow requirements. All use the hydrogen composition distribution τ from the previous moment. k,t-1 calculate.

[0131] 3) Use the flow velocity u obtained in step 2) t Solve for the component distribution τ of the gas mixture at time t. k,t ;

[0132] 4) Using the state variable p at time t t M t Update Z, R, λ, and proceed to step 3) to perform energy flow calculation at time t+1.

[0133] The sequential algorithm yields state variables with some error compared to the direct simultaneous solution, because the pressure and flow distribution at the current moment is calculated using the previous moment's time τ. However, this error is within an acceptable range when the time step is not too large.

[0134] Furthermore, the objective function and constraints of the joint system's low-carbon economic dispatch model are constructed as follows:

[0135]

[0136] In the formula, Y is the total operating cost, which includes the operating expenses C of the coal-fired unit. CFU Renewable energy cost reduction C RCUR Electricity load reduction cost C LCUR Gas purchase cost Carbon emission costs The constraints for the low-carbon economic operation of the hydrogen-electric combined system include: dynamic energy flow constraints, pressure and flow constraints, and equipment hydrogen blending adaptability constraints on the gas grid side; DC power flow constraints, power balance constraints, branch transmission capacity constraints, unit output upper and lower limit constraints, and unit ramping constraints on the grid side; electricity-gas energy conversion constraints, including constraints on hydrogen production by electricity and operation of hydrogen-electric gas turbines; and carbon quota constraints.

[0137] More specifically, the solution process for the joint system's low-carbon economic dispatch problem is as follows:

[0138] 1) Let the iteration number r = 1;

[0139] 2) Relax the gas network operation constraints and solve for the optimal energy flow of the combined system to obtain the output of the hydrogen production and hydrogen-blended gas turbine.

[0140] 3) Converted to nodal hydrogen injection volume Gas demand at load nodes As boundary conditions for numerical simulation of mixed hydrogen natural gas networks;

[0141] 4) The baseline flow velocity u was obtained through numerical simulation of the hydrogen-to-natural gas network. b,r Parameter a, gas parameter ZRλ, are used to construct a linear dynamic energy flow model for a mixed hydrogen natural gas network;

[0142] 5) Call the solver to solve the low-carbon economic dispatch problem of the hydrogen-electric combined system, and calculate the output of the hydrogen-to-electricity and hydrogen-blended gas turbines. Convert to boundary conditions Another numerical simulation of the hydrogen-to-natural gas network was performed to solve for the flow velocity u. r distributed;

[0143] 6) Calculate the error between the actual flow velocity and the reference flow velocity, ε=||u r -u b,r ||;

[0144] 7) Determine if the convergence condition ε≤ε0 is met: If it is met, terminate the calculation; if not, update the boundary conditions. r = r + 1, return to step 3) to enter the next iteration.

[0145] For example, the low-carbon economic dispatch model for a hydrogen-electric combined system is constructed as follows:

[0146] Carbon allowances are allocated using a benchmark method. The carbon emission allowances for conventional coal-fired units and gas-fired units can be represented as follows: In the formula, i is the unit number; For unit i, the carbon emission allowance during time period t; The baseline value for carbon quota allocation for unit i, in units of tCO2 / MWh; P i,t This represents the active power of thermal power unit i during time period t.

[0147] The carbon emissions of conventional coal-fired and gas-fired power units are related to their output, and can be expressed as follows: In the formula, The carbon emissions of thermal power unit i during time period t; The carbon emission intensity of thermal power unit i is expressed in tCO2 / MWh.

[0148] The cost of carbon emissions can be expressed as: In the formula, Emi a τ represents the allowable volume of carbon allowance trading. C This is the carbon emission cost price coefficient.

[0149] Based on the above example data, the objective function of the constructed low-carbon economic dispatch model for the hydrogen-electric combined system is:

[0150]

[0151] In the formula, T N To optimize the total number of time periods; Δt is the scheduling time interval; N g N w N load N src These are the number of coal-fired power units, wind farms, power loads, and natural gas sources, respectively. Let be the fuel cost corresponding to the unit power generation of the i-th coal-fired unit at time t; Let $t$ be the cost of wind curtailment penalty for the $i$-th wind farm at time $t$. Let be the load shedding cost for the i-th electrical load at time t; Let be the gas purchase price at the i-th gas source node at time t.

[0152] The low-carbon economic dispatch model for the hydrogen-electric hybrid system needs to satisfy the constraints of equipment operation, power grid operation, hydrogen-electric hybrid network operation, and carbon emission quotas within the system.

[0153] Specifically, the operational constraints of devices within the system include:

[0154] Operating constraints for conventional coal-fired power units include upper and lower output limits and ramp / decrease constraints: Operating constraints for electro-hydrogen production equipment include energy conversion constraints and output upper and lower limit constraints: Operating constraints for gas turbine units include energy conversion constraints and upper and lower output limits: Power grid operation constraints include power balance constraints, spinning reserve constraints, and branch transmission capacity constraints.

[0155]

[0156] Operating constraints for the hydrogen blending network include pressure and flow rate constraints, and equipment adaptability constraints. Specifically, the mass flow rate of the gas at the source node should be greater than zero, the pressure and flow rate should be within the safe operating range, and the upper and lower limits of hydrogen mass fraction for each piece of equipment within the network should be met.

[0157]

[0158] in, p i,t and These represent the minimum and maximum allowable operating pressures at node i at time t after hydrogen blending; the selection of the upper and lower limits for hydrogen mass fraction mainly considers the operational safety constraints of pipeline equipment. For pipelines, the possibility of hydrogen embrittlement, leakage, and seepage should be considered. For compressors, normal aerodynamic performance and surge margin should be ensured. For end users, gas interchangeability constraints should be met, i.e., calorific value and Wobbe index should both be within safe ranges. Considering carbon trading policies, the joint system should comply with carbon emission quota constraints:

[0159]

[0160] Operating S4, in scenarios involving fluctuations in renewable energy output and changes in the hydrogen blending ratio at hydrogen injection points, allows for online pre-decision making, according to... Figure 3 The iterative process shown solves the optimized scheduling model to obtain an executable joint system scheduling scheme, making full use of the system's flexible resources.

[0161] S5: Execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity combined system and make full use of the system's flexibility resources.

[0162] To further illustrate the low-carbon economic dispatch method for the hydrogen-electric combined system provided in this embodiment, Figure 4 and Figure 5 The main steps for verifying the effectiveness of the system shown are as follows:

[0163] 1) The effectiveness of the dynamic energy flow model of the present invention was verified using a branchless gas network of a single electric hydrogen production unit;

[0164] 2) The effectiveness of the low-carbon economic dispatch method of the present invention is verified by using a combined hydrogen-electricity system consisting of a Garver 6-node power system and a 12-node natural gas system as a simulation analysis example.

[0165] The calculation results of hydrogen mass fraction and source node mass flow rate for the branchless hydrogen-to-natural gas network using steady-state energy flow and linear dynamic energy flow models are as follows: Figure 6 and Figure 7 As shown, the steady-state values ​​calculated by the two models are the same, but the method can better reflect the transition process caused by fluctuations in hydrogen injection flow rate and more accurately reflect the dynamic response process of the gas network after the disturbance occurs.

[0166] Table 1. Scheduling results of the scheduling model considering the mixed transport dynamic process.

[0167]

[0168] As shown in Table 1, the simulation results demonstrate that the low-carbon economic dispatch method for the mixed hydrogen-electricity system can be used to optimize the operation of the mixed hydrogen-electricity system under scenarios with multiple gas sources of different components. Under the premise of ensuring that the gas quality meets the specifications and that the hydrogen concentration at each location is within a safe range, the method optimizes the output of each link, maximizes the use of hydrogen production through water electrolysis and hydrogen storage through natural gas pipelines to assist the power system in absorbing renewable energy, and achieves safe, economical, and low-carbon operation of the system as a whole. It has broad application prospects in integrated energy systems containing electricity, gas, and hydrogen.

[0169] This embodiment also provides an electronic device suitable for low-carbon and economical dispatch of a hydrogen-electric hybrid system, such as... Figure 8 As shown, it includes a parameter acquisition and model building module, and a model solving and execution module. During the scheduling process, the system parameters collected in real time are input, and the above two modules are called to achieve the following: Figure 1 The scheduling function shown solves for the optimal scheduling scheme and issues the corresponding scheduling instructions.

[0170] Example 2

[0171] This embodiment provides a low-carbon and economical dispatch method for a hydrogen-electric hybrid system, which includes the following modules.

[0172] The first construction module is used to take into account the dynamic transport process of mixed gas and the effect of gas parameter distribution, and to construct the partial differential equation system corresponding to the dynamic energy flow of the pipeline network.

[0173] The reconstruction module is used to replace the corresponding nonlinear terms in the partial differential equations corresponding to the dynamic energy flow of the pipeline network with the velocity reference value, the flow reference value, and the hydrogen mass flow rate, so as to reconstruct the partial differential equations into a discretized linear dynamic energy flow equation suitable for solving the optimal scheduling problem.

[0174] The second module is used to construct a low-carbon economic dispatch model for the hydrogen-electric combined system, with the goal of minimizing system operating costs under carbon tax considerations and using discretized linear dynamic energy flow equations as constraints for the operation of the hydrogen-electric combined system. Y represents the total operating cost, including the operating expenses of coal-fired power units (C). CFU Renewable energy cost reduction C RCUR Electricity load reduction cost C LCUR Gas purchase cost Carbon emission costs

[0175] The solution module is used to solve the low-carbon economic scheduling model of the hydrogen-electric combined system and obtain an executable scheduling scheme for the combined system.

[0176] The scheduling module is used to execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity joint system.

[0177] Example 3

[0178] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0179] Example 4

[0180] This embodiment provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the steps of the above method.

[0181] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A low-carbon economic dispatch method for a hydrogen-electricity combined system, characterized in that, include: S1: Taking into account the dynamic transport process of mixed gas and the effect of gas parameter distribution, construct a set of partial differential equations corresponding to the dynamic energy flow of the pipeline network; S2: Replace the corresponding nonlinear terms in the partial differential equations corresponding to the dynamic energy flow of the pipeline network with the speed reference value, flow reference value, and hydrogen mass flow rate, so as to reconstruct the partial differential equations into a discretized linear dynamic energy flow equation suitable for solving the optimal scheduling problem. S3: With the goal of minimizing the system operating cost under carbon tax consideration, and using the discretized linear dynamic energy flow equation as the operating constraint of the mixed hydrogen gas network, a low-carbon economic dispatch model for the mixed hydrogen gas-electricity combined system is constructed. This model can track changes in gas composition during transportation and reflect the operating status of the mixed hydrogen natural gas system. , Y It is the total operating cost, including the operating expenses of coal-fired power units. Renewable energy cost reduction Electricity load reduction costs Gas purchase cost Carbon emission costs ; S4: Solve the low-carbon economic scheduling model of the hydrogen-electric combined system to obtain an executable joint system scheduling scheme; S5: Execute the combined system scheduling scheme to schedule the mixed hydrogen-electricity combined system; Before S2, the process further includes: based on the stability conditions in numerical analysis, reasonably dividing the numerical calculation differential grid, and then using a sequential algorithm to pre-solve the dynamic energy flow of the nonlinear pipeline network to obtain the velocity reference value and the flow reference value; the step size of the differential grid of the energy flow equation satisfies the following conditions: ； in, for t Time Node i Gas flow rate at the location; for t Time Node i Gas flow velocity at -1 It is the time step; It is the spatial step size; S4 includes: S41: Let the iteration number r = 1; S42: Relax the gas network operation constraints and solve for the optimal energy flow of the joint system to obtain the output of the hydrogen production and hydrogen-blended gas turbine. S43: will Converted to nodal hydrogen injection volume Gas demand at load nodes S44: As boundary conditions for numerical simulation of the hydrogen-mixed natural gas network; The baseline flow velocity is obtained through numerical simulation of the hydrogen-mixed natural gas network. u b,r ,parameter a Gas parameters ZR λ S45: Used to construct a linear dynamic energy flow model for a hydrogen-gas combined system; S45: Call the solver to solve the low-carbon economic dispatch problem of the hydrogen-gas-electric combined system, and calculate the output of the hydrogen-to-electricity and hydrogen-blended gas turbines. Convert to boundary conditions Numerical simulation of the hydrogen-to-natural gas network was performed again to solve for the velocity distribution. u r S46: Utilizing velocity distribution u r Calculate the error between the actual flow velocity and the reference flow velocity. S47: Determine if the convergence condition is met. If the conditions are met, the calculation ends; otherwise, the boundary conditions are updated. ; r = r +1, return to S43 to enter the next iteration.

2. The low-carbon economic dispatch method for the hydrogen-electric combined system as described in claim 1, characterized in that, In S1: The partial differential equations corresponding to the dynamic energy flow in a single pipe include: the momentum conservation equation for the mixed gas, the continuity equation, and the hydrogen component transport equation, as follows: ； ； ； in, t and x These represent the distance along the pipe axis and the time, respectively; ρ This refers to the density of the mixed gas, expressed in kg / m³. 3 ; u The velocity of the mixed gas is expressed in m / s. p Pressure, unit is Pa; λ is the hydraulic friction coefficient, dimensionless; d The diameter of the pipe; The first in the mixed gas k The mass fraction of each component.

3. The low-carbon economic dispatch method for the hydrogen-electric combined system as described in claim 2, characterized in that, Constraints on gas density, pressure, and composition at pipeline source nodes: ； in, express t Time Node i At the gas density, This indicates the gas density of the gas source. express t Time Node i At air pressure, Indicates the air pressure of the gas source. express t Time Node i The mass fraction of hydrogen gas is determined. This indicates the mass fraction of hydrogen in the gas source. Indicates the total number of scheduling periods; Flow constraints at load nodes in the last segment of the pipeline network: ； in, for t Time Node i Mass flow rate, for t Time-based load nodes i The mass flow rate requirement for mixed hydrogen natural gas for t Time Node i The natural gas mass flow rate requirement at the load condition does not consider hydrogen blending. for t Time Node i The mass fraction of hydrogen in the mixed hydrogen natural gas. , These represent the calorific value of natural gas and hydrogen, respectively. Gas flow balance constraints: and ; Uniform mixing constraint: ; in, Representatives and nodes i Number of connected pipes; , for t Time and Node i The first connection j Root pipe inflow / outflow node i mass flow rate; for t Time Node i The mass flow rate of the hydrogen-mixed natural gas injected at the site; For nodes i The mass flow rate of hydrogen in the injected mixed hydrogen natural gas; , for t Time and Node i The first connection j Root pipe inflow / outflow node i The mass fraction of hydrogen in the gas; For nodes i Hydrogen mass fraction at the load node; For nodes i The required mass flow rate of hydrogen-mixed natural gas at the specified load; For nodes The mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at the point.

4. The low-carbon economic dispatch method for the hydrogen-electric combined system as described in claim 3, characterized in that, The linearized and reconstructed partial differential equations for single-pipe flow are as follows: ； ； ； in, The mass flow rate of the mixed gas; This refers to the cross-sectional area of ​​the pipe. It is the compression factor; is the gas constant of the gas mixture, expressed in J / (kg·K); Temperature is the thermodynamic temperature, and its unit is K. This is the reference value for gas flow rate; This refers to the hydrogen mass flow rate; A coefficient related to the mass flow rate of the mixed gas; The reconstructed gas flow balance constraints and uniform mixing constraints are as follows: and ; in, , for t Time and Node i The first connection j Root pipe inflow / outflow node i The mass flow rate of hydrogen gas; For nodes i Hydrogen mass flow rate at the load node; for t Time and Node i The first connection j Root channel outflow node i The reference value for gas flow rate.

5. The low-carbon economic dispatch method for the hydrogen-electric combined system as described in claim 1, characterized in that, The step of pre-solving the dynamic energy flow of the nonlinear pipeline network using a sequential algorithm to obtain velocity and flow rate reference values ​​includes: 1) Solve the steady-state energy flow equation using Newton's iterative method, and use it as the initial condition for dynamic energy flow calculation; 2) Solve the pressure-flow equation in the nonlinear pipeline dynamic energy flow using Newton's iteration method to obtain the pressure... and mass flow Then, the flow rate, gas parameters, and load flow requirements are solved. ; 3) Use the flow rate obtained in step 2) Solve Distribution of gas components at any time ; 4) Use Time-state variables , Update the gas parameters and proceed to step 3). The energy flow calculation at each moment ultimately yields the velocity and flow rate reference values ​​in the linearized processing.

6. A low-carbon, economical dispatching device for a hydrogen-electricity combined system, characterized in that, The method for implementing the low-carbon economic dispatch method according to any one of claims 1-5 includes: The first construction module is used to take into account the dynamic transport process of mixed gas and the effect of gas parameter distribution, and to construct the partial differential equation system corresponding to the dynamic energy flow of the pipeline network. The reconstruction module is used to replace the corresponding nonlinear terms in the partial differential equations corresponding to the dynamic energy flow of the pipeline network with the velocity reference value, the flow reference value, and the hydrogen mass flow rate, so as to reconstruct the partial differential equations into a discretized linear dynamic energy flow equation suitable for solving the optimal scheduling problem. The second module is used to construct a low-carbon economic dispatch model for the mixed hydrogen gas-electricity system with the optimization objective of minimizing the system operating cost under the consideration of carbon tax and the discretized linear dynamic energy flow equation as the operating constraint of the mixed hydrogen gas network. It can track the changes in gas composition during transportation and reflect the operating status of the mixed hydrogen natural gas system. , Y It is the total operating cost, including the operating expenses of coal-fired power units. Renewable energy cost reduction Electricity load reduction costs Gas purchase cost Carbon emission costs ; The solution module is used to solve the low-carbon economic scheduling model of the hydrogen-gas-electric combined system to obtain an executable joint system scheduling scheme; The scheduling module is used to execute the joint system scheduling scheme to schedule the mixed hydrogen-electricity joint system.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images